U.S. patent number 11,255,990 [Application Number 16/646,197] was granted by the patent office on 2022-02-22 for internal structure detection system.
This patent grant is currently assigned to HITACHI, LTD.. The grantee listed for this patent is HITACHI, LTD.. Invention is credited to Akira Maeki, Shiro Mazawa, Tatsuyuki Saito, Takao Sakurai, Tomonori Sekiguchi.
United States Patent |
11,255,990 |
Maeki , et al. |
February 22, 2022 |
Internal structure detection system
Abstract
An internal structure detection system includes: two kinds of
sensors with different operating principles for receiving reflected
waves of vibration applied to an inspection target in an
investigation area; and a processing apparatus that detects an
internal structure of the inspection target by using the sensor
data received by the two kinds of sensors. The two kinds of sensors
are deployed in the investigation area with different densities, in
a distributed manner.
Inventors: |
Maeki; Akira (Tokyo,
JP), Mazawa; Shiro (Tokyo, JP), Saito;
Tatsuyuki (Tokyo, JP), Sakurai; Takao (Tokyo,
JP), Sekiguchi; Tomonori (Tokyo, JP) |
Applicant: |
Name |
City |
State |
Country |
Type |
HITACHI, LTD. |
Tokyo |
N/A |
JP |
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Assignee: |
HITACHI, LTD. (Tokyo,
JP)
|
Family
ID: |
1000006132466 |
Appl.
No.: |
16/646,197 |
Filed: |
October 25, 2017 |
PCT
Filed: |
October 25, 2017 |
PCT No.: |
PCT/JP2017/038452 |
371(c)(1),(2),(4) Date: |
March 11, 2020 |
PCT
Pub. No.: |
WO2019/082292 |
PCT
Pub. Date: |
May 02, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20200278463 A1 |
Sep 3, 2020 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01V
1/168 (20130101); G01V 1/182 (20130101); G01V
2210/144 (20130101) |
Current International
Class: |
G01V
1/16 (20060101); G01V 1/18 (20060101) |
Field of
Search: |
;367/7 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2016-085085 |
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May 2016 |
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JP |
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2017/141304 |
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Aug 2017 |
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WO |
|
Other References
Translation of desorption of et al. (JP 2016085085). 47 pages.
(Year: 2016). cited by examiner .
Translation of desorption of Maeki et al. (WO 2017/141304), 37
pages. (Year: 2017). cited by examiner .
Fumitoshi Murakami, et al. Evaluation of MEMS Accelerometer Sensor
for Earthquake Observations, Bulletin of the Earthquake Research
Institute, University of Tokyo, vol. 84, 2009, pp. 251-266 abstract
only in English. cited by applicant .
International Search Report and Written Opinion of
PCT/JP2017/038452 dated Jan. 9, 2018. cited by applicant.
|
Primary Examiner: Murphy; Daniel L
Attorney, Agent or Firm: Mattingly & Malur, PC
Claims
The invention claimed is:
1. An internal structure detection system comprising: two kinds of
sensors with different operating principles for receiving reflected
waves of vibration applied to an inspection target in an
investigation area; and a processing apparatus that detects an
internal structure of the inspection target by using the sensor
data received by the two kinds of sensors, wherein the two kinds of
sensors are deployed in the investigation area with different
densities, in a distributed manner, wherein the processing
apparatus comprises a storage unit storing sensor information
including noise spectral density for each sensor used for
inspecting interior of the investigation area, the sensor
information being associated with unique identification information
of the sensor, and wherein the sensor information is associated
with validity coefficients representing which frequency bands of
the sensor data obtained by the corresponding sensor are valid, the
validity coefficients reflecting a result of inspecting the
corresponding sensor at predetermined intervals.
2. The internal structure detection system according to claim 1,
wherein one of the two kinds of sensors is first sensors having a
low noise density in a low frequency region, wherein the other one
of the two kinds of sensors is second sensors having a high noise
density in a low frequency region, and wherein the first sensors
are deployed in the investigation area in such a manner that the
first sensors are distributed more sparsely than the second
sensors.
3. The internal structure detection system according to claim 2,
wherein the first sensors having the low noise density in the low
frequency region are MEMS sensors and the second sensors having the
high noise density in the low frequency region are geophone
sensors.
4. The internal structure detection system according to claim 3,
wherein measurement frequency bands of the two kinds of sensors are
at least overlapped.
5. The internal structure detection system according to claim 2,
wherein measurement frequency bands of the two kinds of sensors are
at least overlapped.
6. The internal structure detection system according to claim 1,
wherein the two kinds of sensors are geophone sensors and MEMS
sensors.
7. The internal structure detection system according to claim 6,
wherein measurement frequency bands of the two kinds of sensors are
at least overlapped.
8. The internal structure detection system according to claim 1,
the processing apparatus selects data from the sensor data of the
two kinds of sensors used for detecting the internal structure in a
manner that depends on a distance from a surface of the inspection
target to which vibration is applied to where the reflected wave
resulting from the vibration has been reflected.
9. The internal structure detection system according to claim 8,
wherein measurement frequency bands of the two kinds of sensors are
at least overlapped.
10. The internal structure detection system according to claim 1,
wherein measurement frequency bands of the two kinds of sensors are
at least overlapped.
11. The internal structure detection system according to claim 1,
wherein a value of "1" for a validity coefficient at a particular
frequency means that data at that frequency is to be used and a
value of "0" for the validity coefficient at the particular
frequency means that the data at that frequency is not to be used.
Description
TECHNICAL FIELD
The present invention relates to a system that detects an internal
structure.
BACKGROUND ART
As a method of detecting an internal structure of a construction,
capturing an elastic wave using a speed sensor or an acceleration
sensor and analyzing data of the captured elastic wave and thereby
detecting the internal structure of the construction is widely
practiced. Such a method is applied not only to later-described
resource exploration but also to non-destructive testing by
ultrasound probe, inspection of bridges, buildings, tunnels or the
like, and water leakage inspection of water pipes. Hereinafter, a
resource exploration is described as an example of such
application. In petroleum and gas industries, large-size reservoirs
(petroleum and gas reservoirs), which are easy to extract petroleum
and/or gas, have already been discovered and developed. Henceforth,
exploration at a deeper depth and in a complex stratum is required.
Improvement in sensitivity of sensors and/or high-density
exploration is important to achieve such exploration. In addition,
a lower cost of operation is demanded from the market.
A method referred to as physical exploration or reflection seismic
exploration is present as a method widely used in resource
exploration. In principle, elastic waves generated by an artificial
seismic source (such as a dynamite, a vibrator vehicle that
vibrates the ground, and the like) are reflected at an interface of
a stratum, for example, an interface of a petroleum layer, gas
layer, water, a rock layer, etc.; reflected waves returning to the
ground surface are detected by a plurality of sensors deployed on
the ground surface or a borehole; and an underground structure
image is constructed from data of these reflected waves.
Geophones, which are speed sensors utilizing a coil and a magnet,
are widely used as sensors for detecting reflected waves. However,
in the case of geophones, it is practically difficult to improve
the sensitivity in a low frequency region in the range of several
hertz and to obtain flat frequency characteristics. Thus, there is
a demand for another sensor mechanism that resolve these problems.
In this context, a high sensitive sensor using a micro
electromechanical system (MEMS) technology, which utilizes
conventional semiconductor circuits and mechanical operations, is
disclosed for example in Patent Literature 1 "INERTIAL SENSOR".
PRIOR ART DOCUMENT
Patent Literature
Patent Literature 1: United State Patent Application Publication
No. 2016/0091524
SUMMARY OF THE INVENTION
Technical Problem
The use of the technique disclosed in Patent Literature 1 or the
like makes it possible to construct a MEMS sensor which is highly
sensitive in the low-frequency region and superior in the frequency
characteristics. However, MEMS sensors have a shorter history for
use in resource explorations and more complex structures than the
geophones and thus differ from the geophones in terms of variations
in products, long-term reliability, and price.
In addition, it is presumed that during an introduction period of a
new sensor, e.g., MEMS sensor, exploration operations using both
the geophones owned by an exploration service company and the MEMS
sensors would be carried out, in which case the performance
difference between sensors with different types will be large.
Exploration data collected by sensors is rendered as an underground
image of the exploration area by an analysis algorithm implemented
in a computer, which underground image is used for petroleum/gas
reservoir estimation. It has been known that computation using high
accurate and highly reliable data contributes to improvement of the
quality and precision of the estimation. For example, for an
analysis method called full waveform inversion (FWI), which has
been paid attention and put into practice in recent years, it is
known that the quality and precision of the exploration data in a
low frequency region is more important.
The geophones, having used so far, have a simple structure and have
not so large variations in products and would not be so much
degraded, and, moreover, take the same form of a speed sensor made
up of a coil and a magnet. Accordingly, in the case of resource
exploration using geophones, the resource explorations have been
conducted by performing: without serious consideration of
variations in products of geophones, deployment of the geophones to
the field (arranging sensors to predetermined positions),
exploration operations (causing the ground to vibrate by a vibrator
vehicle, capturing a reflected wave reflected from a reflection
surface in the underground by the sensors and extending the
exploration area while rearranging the deployed sensors, and the
like), and analysis and interpretation (estimating the underground
structure on the basis of the data acquired by the sensors and
stratum samples).
However, upon introduction of MEMS sensors, consideration needs to
be made of the fact that, as described above, the sensitivity
characteristics would greatly vary due to the sensor type and
variations in products; and consideration needs to be made of how a
correct analysis result can be obtained with evolution of analysis
technique represented by FWI, under the given condition of the
variations in sensor sensitivity.
Furthermore, in the case of FWI, due to its calculation process,
the quality of data in a low frequency region has significant
effect to the final output. Therefore, it is important to extract
more highly reliable and more highly accurate data from the
obtained data.
The present invention resolves the above-described problems, and it
is an object of the present invention to provide an internal
structure detection system capable of acquiring highly reliable and
highly accurate data about an investigation area.
Solutions to Problem
To achieve the above-described object, an internal structure
detection system of the present invention includes: two kinds of
sensors with different operating principles (e.g., geophone sensor
terminals 302, MEMS sensor terminals 310) for receiving reflected
waves of vibration applied to an inspection target in an
investigation area; and a processing apparatus (e.g., processing
apparatus 120) that detects an internal structure of the inspection
target by using the sensor data received by the two kinds of
sensors. The two kinds of sensors are deployed in the investigation
area with different densities, in a distributed manner. Other
aspects of the present invention are described with an embodiment
described later.
Advantageous Effects of the Invention
The present invention makes it possible to acquire highly reliable
and highly accurate data in an investigation area.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 is a diagram illustrating an example of resource exploration
according to an internal structure detection system of a present
embodiment.
FIG. 2 is a diagram illustrating an example of a sensor
terminal.
FIG. 3 is a diagram illustrating a layout example in which a
plurality of types of sensor terminals are deployed.
FIG. 4 is a diagram illustrating another layout example in which a
plurality of types of sensor terminals are deployed.
FIG. 5 is a diagram illustrating sensor characteristics expected of
a geophone array and a MEMS sensor.
FIG. 6 is a diagram illustrating an example of improved sensor
characteristics of the geophone array.
FIG. 7 is a diagram illustrating an example of an altered sensor
characteristic of the MEMS sensor.
FIG. 8 is a diagram illustrating the frequency characteristics of
the sensor terminals and an example of utilization of data used in
calculation.
FIG. 9 is a diagram illustrating the frequency characteristics of
the sensor terminals and an example of another utilization of data
used in calculation.
FIG. 10 is a diagram illustrating an example of resource
exploration, in a three dimensional fashion.
FIG. 11 is a diagram illustrating which sensors are to be used in
resource exploration.
FIG. 12 is a diagram illustrating sensor inspection
information.
FIG. 13 is a diagram illustrating an example of the sensor
characteristics information of a geophone sensor.
FIG. 14 is a diagram illustrating an example of the sensor
characteristics information of a MEMS sensor.
FIG. 15 is a diagram illustrating an example of the sensor
characteristics information of another MEMS sensor.
DESCRIPTION OF EMBODIMENTS
An embodiment for carrying out the present invention is described
in detail with reference to the drawings as appropriate.
FIG. 1 is a diagram illustrating an example of resource exploration
according to an internal structure detection system of a present
embodiment. FIG. 1 illustrates a simplified configuration to
describe points of the invention. However, sensors and/or shot
points may not necessarily be arranged in an orderly manner as
shown in the figure, due to various factors in the actual
field.
One or a plurality of vibrator vehicles 100 constitute one group of
vibrator vehicles 101a and move to a shot point 102 and generate
seismic waves. For example, the group of vibrator vehicles 101a may
be constituted by four vibrator vehicles 100. In FIG. 1, only one
shot point 102 is illustrated as the shot point. However, all the
intersections of the lattice illustrated in FIG. 1 are each a shot
point. Accordingly, the group of vibrator vehicles 101a generates
seismic waves at the shot points corresponding to the intersections
of the lattice, while moving substantially straight on a movement
path 104a.
Upon having moved to the shot points 102 and generated seismic
waves, the group of vibrator vehicles 101a makes a U-turn and
generates seismic waves, while moving on a movement path 104b. In
this way, the group of vibrator vehicles 101a generates seismic
waves at shot points set in advance in the investigation area,
e.g., at all the intersection of the lattice illustrated in FIG. 1
by repeating a substantially straight movement and a U-turn. For
example, the shot points are set at certain intervals determined in
advance, for example at intervals of 50 m. The position of a shot
point is located using a global positioning system (GPS) signal
from a satellite 105, for example.
For example, 100 thousand shot points are specified in a manner
that depends on a region of the investigation area (exploration
area), etc. As the number of the shot points is large, the
exploration is performed in the same period of time by dividing the
exploration area into a plurality of regions assigned to a
plurality of groups of vibrator vehicles 101 including the group of
vibrator vehicles 101a (referred to as group of vibrator vehicles
101 when the group of vibrator vehicles 101a is not specifically
referred to and the group of vibrator vehicles in FIG. 1 is
generally referred to; and other reference symbols are referred to
in the same manner), shifting the timing for generating seismic
waves to avoid adverse effects, and/or embedding code in the
waveform of the generated seismic waves.
It should be noted that although the group of vibrator vehicles 101
is represented by one row in FIG. 1, it may be organized in a
plurality of rows, e.g., in two rows. In some cases, depending on
the exploration area and/or the density of the shot points, a group
of four vibrator vehicles 101 in two rows each with two vibrator
vehicles 101 can be preferred compared to a group of four vibrator
vehicles 101 in one row. FIG. 1 shows a group of four vibrator
vehicles 100 as an example of the group of vibrator vehicles 101.
However, the group of vibrator vehicles 101 may consist of one
vibrator vehicle 100. Vibration energies of the group of vibrator
vehicles 101 are measured and recorded in advance irrespective of
the configuration of the group of vibrator vehicles 101. The group
of vibrator vehicles 101a repeats generation of seismic waves and
movement to move to the location of the group of vibrator vehicles
101b.
The vibration caused by the seismic wave generation of the group of
vibrator vehicles 101b is reflected, for example, by a boundary
surface between a stratum such as a rock layer and a reservoir 110
containing petroleum and/or gas and becomes a reflected wave 112a.
The reflected wave 112a can be detected by a sensor terminal 103,
for example. Signals of the reflected wave 112a or the like
detected by the sensor terminal 103 are collected by an observation
vehicle 106, etc., and then analyzed by a processing apparatus 120.
Also, a surface wave 111a, which though is vibration not necessary
for exploration, travels from the group of vibrator vehicles 101b
to the sensor terminal 103 on a ground surface. Use of the surface
wave 111a will be described later.
The sensor terminal 103 detects vibrations of the surface wave 111a
and the reflected wave 112a as acceleration. In general, the
distance from the group of vibrator vehicles 101b to the sensor
terminal 103 is shorter than the distance from the group of
vibrator vehicles 101b to the sensor terminal 103 via a reservoir
110. Therefore, after the time of generating seismic waves, the
acceleration 111b resulting from the vibration of the surface wave
111a is detected earlier than when the acceleration 112b resulting
from the vibration of the reflected wave 112a is detected, and the
detected value of the acceleration 111b is greater than that of the
acceleration 112b.
As illustrated in FIG. 1, a plurality of sensor terminals 103 is
arranged. Similarly to the shot points, for example, 100 thousand
sensor terminals 103 are arranged in a manner that depends on the
regions or the like of the exploration area. Normally, the sensor
terminals 103 are arranged in areas overlapping with the movement
path 104 of the group of vibrator vehicles 101. However, the sensor
terminals 103 may be arranged in areas not overlapping with the
movement path 104 of the group of vibrator vehicles 101. The
exploration area may be a desert or an urban area or the like.
The sensor terminals 103 are deployed before the group of vibrator
vehicles 101 starts the first generation of seismic waves and are
collected after the group of vibrator vehicles 101 finishes the
last generation of seismic waves. Alternatively, the exploration
may be performed, while rearranging the sensor terminals 103 in
order to extend the exploration area, by the group of vibrator
vehicles 101 generating vibrations and the sensor terminals 103
performing measurement. For this reason, there is a possibility
that the sensor terminals 103 be deployed in a harsh environment
for a long time.
FIG. 2 is a diagram illustrating an example of the sensor terminal
103. Here, description will be given of a case in which a MEMS
acceleration sensor 201 is used in the sensor terminal 103. A MEMS
acceleration sensor 201 is an acceleration sensor using MEMS
technology. The MEMS acceleration sensor 201 detects vibration
applied to the sensor terminal 103 such the surface wave 111a, the
reflected wave 112a, or the like as acceleration and converts the
detected acceleration into an electrical signal. The MEMS
acceleration sensor 201 has high sensitivity. On the other hand,
however, as a microstructure is adopted and a hard material
mechanically vibrates, the MEMS acceleration sensor 201 may be
subject to deterioration or damage due to an influence from the
outside, not only by a strong impact but also by rapid thermal
cycle and/or operations at high or low temperature. There exist a
telemetry system and a nodal system. The telemetry system is a
system in which sensor terminals are connected with cables to
collect information in a real-time manner, control the terminal,
and supply power to the terminals. The nodal system is a cableless
system in which the sensor terminals 103 themselves operate
independently and hold the acceleration data or the like by
themselves.
The MEMS acceleration sensor 201 has a movable part and a fixed
part to which vibration from the outside is transmitted and which
is connected via an elastic body to the movable part. The MEMS
acceleration sensor 201 is configured to determine an acceleration
on the basis of, for example, the variation in the capacity values
of the fixed part and movable part that occurs due to the external
vibration.
In general, due to these factors and problems related to the
characteristics of the materials of the MEMS acceleration sensor
201 and to the package or the like thereof, the degree of vacuum in
the space between the fixed part and the movable part may decrease
for example, resulting in a change in the elastic characteristics
of the elastic body. As a result of an increase in the detectable
minimum vibration width, the minimum vibration width may increase
to exceed a predetermined threshold value and leads to such a
damage that no vibration can be detected. There have been several
measures taken to address these problems.
It should be noted that although the MEMS acceleration sensor 201
is typically an electrostatic capacitance detection acceleration
sensor, it is not limited thereto. The MEMS acceleration sensor 201
may be based on another configuration, in which case, however,
externally imposed different factors need to be taken into account.
The MEMS acceleration sensor 201 may be a sensor or a sensor chip
alone. Besides, the MEMS acceleration sensor 201 may include a
certain circuit. In addition, the MEMS acceleration sensor 201 may
include a package for covering those parts. Moreover, the MEMS
acceleration sensor 201 may be a speed sensor rather than an
acceleration sensor.
The sensor terminal 103 includes the above-described MEMS
acceleration sensor 201, an acceleration signal processor 202, an
external interface (I/F) 203, a processor 204, a storage unit 205,
a GPS receiver 206, an environmental sensor 207, and a battery 208.
Alternatively, in the case of the above-described telemetry system,
the sensor terminal 103 may be configured such that it depends on
external devices with regard to the storage unit 205 and/or the
battery 208.
The processor 204 controls each part of the sensor terminal 103
according to a program stored in advance. When operation parameter
setting needs to be made in the acceleration signal processor 202,
the external I/F 203, the GPS receiver 206, and the environmental
sensor 207, the processor 204 sets the operation parameters
according to a program.
The processor 204 may include one or a plurality of timers. The
timer counts time from a preset time. The preset time may be a time
at which the sensor terminal 103 including the processor 204 is
manufactured, a time at which the MEMS acceleration sensor 201 is
manufactured, or a specific standard time. The counting continues
without stopping during the period for counting. The period for
counting may be power supply periods and the counting may be
started and suspended on the basis of predetermined conditions. The
counted periods may be used for the management of the regular
inspection of the sensor.
The processor 204 may be a so-called microprocessor (single-chip
microcomputer). The processor 204 may include a storage unit, and a
program or data stored in the storage unit 205 may be stored in the
storage unit included in the processor 204.
The acceleration signal processor 202 amplifies the electrical
signal which has been converted from the acceleration by the MEMS
acceleration sensor 201, converts the amplified analog electric
signal into an acceleration value of a digital electrical signal,
and corrects the digital electrical signal in accordance with a
detection characteristics of the MEMS acceleration sensor 201. The
acceleration signal processor 202 includes a feedback circuit for
the purpose of the amplification and correction. The feedback
circuit may be configured to perform a feedback operation according
to an amplification ratio parameter and a correction ratio. The
acceleration signal processor 202 may be an application specific
integrated circuit (ASIC).
The external interface (I/F) 203 is an interface communicating with
the outside of the sensor terminal 103. The external I/F 203 may be
a wireless communication interface, a wired communication
interface, or an interface performing wireless communication and
wired communication.
The external I/F 203 transmits the acceleration value which has
been subjected to the conversion performed by the acceleration
signal processor 202. Here, in accordance with instructions from
the processor 204, the acceleration value may be temporarily stored
in the storage unit 205 for the sake of data protection, and then
an acceleration value read out of the storage unit 205 may be
transmitted. The external I/F 203 transmits a value detected by the
environmental sensor 207. The external I/F 203 transmits
information input from the processor 204 and outputs received
information to the processor 204. The above-described is a case of
telemetry system. In a case of nodal system, the obtained data is
stored in the storage unit 205 during the deployment period, then
after the collection of the sensor terminal 103 is finished, the
data is stored in an external storage via the external I/F 203.
FIG. 2 illustrates a case in which the environmental sensor 207 is
included in the sensor terminal 103. However, the sensor terminal
103 may be configured to obtain equivalent data through another
implementation configuration.
The storage unit 205 stores a program for the processor 204, and
stores data necessary for the processor 204 to execute the program.
The storage unit 205 stores values outputted by the acceleration
signal processor 202, the GPS receiver 206, and the environmental
sensor. Further, the storage unit 205 stores a sensor ID (sensor
identification information) as information identifying the MEMS
acceleration sensor 201. Further, the storage unit 205 stores the
later-described noise spectral density (see FIG. 5) (sensor
characteristics information) of the MEMS acceleration sensor 201 in
the form of a formula, a table, or a model, in association with the
sensor ID.
The processing apparatus 120 (see FIG. 1) includes a processing
unit 121 and a storage unit 122. The processing apparatus 120
retrieves sensor characteristics information from sensor terminals
103 under possession before performing exploration on the
exploration area, which is the investigation area. The sensor
characteristics information is stored in the storage unit 122.
Alternatively, the sensor characteristics information can be stored
on a proprietary database and/or cloud system of a company, such as
the manufacturer of the MEMS acceleration sensor 201 and/or the
sensor terminals 103, an owner company of the sensor terminals 103,
an analysis company, or a petroleum company, in association with
the sensor IDs. A description will be given of an example of
database in the processing apparatus 120 with reference to FIGS. 12
to 15.
FIG. 12 illustrates sensor inspection information. The storage unit
122 of the processing apparatus 120 stores sensor inspection
information 122A in possession. The sensor inspection information
122A includes sensor ID, model type, inspection date 1, inspection
result 1, inspection date 2, inspection result 2, and the like. The
sensor is inspected at predetermined intervals with regard to
sensor characteristics. The inspection date and the inspection
result of each inspection is recorded.
As described above, the sensor ID is a unique identifier assigned
to the speed sensor or acceleration sensor, which detects
vibration. A sensor ID starting with "G" represents a geophone
sensor; and a sensor ID starting with "M" represents to MEMS
sensor. At least the measurement frequency bands of the two kinds
of sensors (geophone sensor and MEMS sensor) are overlapped.
As to the inspection results, in the case of sensor ID with
"G000001", the inspection result 1 and inspection result 2 are both
"OK" (no alteration found in sensor characteristics); whereas in
the case of sensor ID with "M000002", the inspection result 1 is
"OK" but inspection result 2 is "CHARACTERISTIC ALTERATION", which
indicates that alteration of the sensor characteristics was found.
Sensor characteristics information of each sensor will be described
with reference to FIGS. 13, 14, and 15.
FIG. 13 is a diagram illustrating an example of the sensor
characteristics information of a geophone sensor. The sensor
characteristics information 122B includes a sensor ID, noise
densities at frequencies, validity coefficients at frequencies and
the like. The noise densities at frequencies are obtained by
quantifying the noise spectral density shown in FIG. 5 described
later. A value of "1" of validity coefficient at a frequency means
that data at that frequency is to be used and a value "0" means
that the data at that frequency is not to be used.
As shown in FIG. 13, in the case in which the sensor ID is
"G000001", as the noise densities at lower frequencies are high,
the validity coefficients at 1 Hz, 2 Hz, 3 Hz, . . . are each
specified as "0". The processing unit 121 of the processing
apparatus 120 sets the validity coefficient at a frequency to "0"
when the noise density at that frequency is greater than or equal
to a predetermined threshold value. Alternatively, the validity
coefficients may be specified by a manager on the basis of a
preliminary investigation on the investigation area.
FIG. 14 is a diagram illustrating an example of the sensor
characteristics information of a MEMS sensor. The sensor
characteristics information 122C includes, like the sensor
characteristics information 122B, a sensor ID, noise densities at
frequencies, validity coefficients at frequencies and the like. As
shown in FIG. 14, in the case in which the sensor ID is "M000001",
as the noise densities at lower frequencies are low, for example,
the validity coefficients at 1 Hz, 2 Hz, 3 Hz, . . . are each
specified as "1".
FIG. 15 is a diagram illustrating another example of the sensor
characteristics information of a MEMS sensor. The sensor
characteristics information 122D includes, like the sensor
characteristics information 122B, a sensor ID, noise densities at
frequencies, validity coefficients at frequencies and the like. As
shown in FIG. 15, in the case in which the sensor ID is "M000002",
as the inspection result 2 shown in FIG. 12 indicates
"CHARACTERISTIC ALTERATION" and the noise densities at lower
frequencies near 1 Hz and 2 Hz has become high, the validity
coefficients at 1 Hz and 2 Hz are each specified as "0".
Returning to FIG. 2, when the sensor terminal 103 includes a
plurality of MEMS acceleration sensor 201, the sensor terminal 103
has a plurality of sensor IDs, in which case a piece of sensor
characteristics information is stored in association with each
sensor ID. Each sensor ID identifies a MEMS acceleration sensor
201. When the sensor terminal 103 includes a plurality of MEMS
acceleration sensor 201, the ID of each MEMS acceleration sensor
201 may be a combination of an ID identifying the sensor terminal
103 and an ID identifying the MEMS acceleration sensor 201.
The GPS receiver 206 receives a GPS signal and outputs location
information of the sensor terminal 103. The processor 204 stores
the location information outputted by the GPS receiver 206 together
with a value outputted by the acceleration signal processor 202
and/or a value outputted by the environmental sensor 207 in the
storage unit 205 and transmits them using the external I/F 203.
When the location of the sensor terminal 103 is determined by
another device when the sensor terminal 103 is deployed, the GPS
receiver 206 may not be present. In addition, the GPS receiver 206
may receive the GPS signal and perform time synchronization.
The environmental sensor 207 includes a temperature sensor that
detects, for example, a temperature of the MEMS acceleration sensor
201 or an external temperature of the sensor terminal 103. The
environmental sensor 207 may include a humidity sensor and include
an acceleration sensor separately from the MEMS acceleration sensor
201. The separate acceleration sensor may be an acceleration sensor
manufactured in the same time period as, or in a time period
different from, that of the MEMS acceleration sensor 201; may be an
acceleration sensor manufactured in the same manufacturing lot as,
or in a manufacturing lot different from, that of the MEMS
acceleration sensor 201; or may be an acceleration sensor having a
structure different from that of the MEMS acceleration sensor
201.
The processor 204 compares the value outputted by the acceleration
signal processor 202 or the environmental sensor 207 with a preset
threshold value. When processor 204 determines that the output
value has exceeded the threshold value, the processor 204 may store
the values outputted from the acceleration signal processor 202,
the environmental sensor 207, and the GPS receiver 206, together
with a count value of the timer in the storage unit 205, and may
transmit the values from the external I/F 203.
In addition, when the count of the timer has reached a value of a
preset interval, the processor 204 may store the values outputted
from these components together with the count value of the timer in
the storage unit 205 and transmit the values from the external I/F
203. The battery 208 supplies power to each circuit in the sensor
terminal.
Although not illustrated, the geophones primarily used currently
are each a speed sensor made up of a magnet and a coil. By fixing
the magnet and/or the coil to a housing of the sensor terminal,
movement of the ground generates a relative movement between the
magnetic field and the coil, causing an electromotive force
proportional to the velocity of the relative movement to be
generated in the coil. On the basis of the value of the
electromotive force, the movement of the ground is observed.
Specifically, the geophone is made up of a coil fixed to a pendulum
suspended via a spring and a permanent magnet fixed to a casing of
a vibration receiver, which gets vibrated in the same vibration as
that of the ground surface. When the casing of the vibration
receiver gets vibrated according to the ground vibration, a
relative movement according to the ground vibration occurs between
the magnet and the pendulum, causing an electromotive force in the
coil. The electromotive force is proportional to the velocity of
the relative movement.
Parameters defining the specification of the geophone include an
eigenfrequency, damping (attenuation constant), sensitivity.
Geophones with an eigenfrequency of 10 Hz to 15 Hz or the like are
often used in resource explorations. A value of about 0.7 is used
as the attenuation constant so that a flat amplitude spectrum can
be obtained. As the geophones take the simple structure as
described above, the number of elements prone to deterioration
and/or breakage is small compared to MEMS sensors and therefore
diagnosis and repair of the geophones are relatively easy.
FIG. 3 is a diagram illustrating a layout example in which a
plurality of types of sensor terminals are arranged. A description
will be given with reference to FIG. 1 as well as appropriate. In
response to a request for exploration with an exploration area and
a time period specified by a petroleum company or client, an
exploration service company will design an exploration plan
suitable to the sensors and the vibrator vehicles owned by the
company. The exploration plan specifies when and in what layout the
sensors are to be deployed and the locations of the shot points at
which seismic waves are to be generated. In such an occasion, it is
widely practiced that when the number of sensors is not enough in a
large-scale exploration, the sensors are rearranged to cover the
predetermined exploration area. The figure illustrates an example
in which sensor terminals are constituted by a geophone array 301
and MEMS sensor terminals 310.
The geophone array 301 is constituted by a plurality of geophone
sensor terminals 302 and an analog-to-digital convertor (ADC) 303,
which converts analog vibration data to a digital signal, and the
like. The use of the array makes it possible to improve the
SN-ratio of the reflected wave and restrain the surface wave
(elastic wave directly propagated from the vibrator vehicle along
the ground surface: 111a in FIG. 1) that becomes a noise component.
The present embodiment presents an example using the geophone array
301. However, alternatively, a large number of geophone sensor
terminals 302 each of which is equipped with an ADC may be
deployed.
In FIG. 3, illustration of the cables (providing data and power and
the like) connecting the sensors is omitted.
The use of geophones taking a form of being connected with cables
is a mainstream approach. This form is called telemetry system and
allows the observation vehicle 106 to observe the status of the
sensors and the data of the generated seismic waves. Alternatively,
in a form called nodal system, each of the sensors includes a
battery and a data storage unit and stores data during the
exploration period, and the data is retrieved from a rack or the
like in the event of collection of the sensor terminals.
The nodal form is taken in cases in which cable connection is
difficult due to a site location such as when a river is present or
in cases when handling of cables is complicated due to a huge
number of cables.
The system according of the present embodiment has: two kinds of
sensors (geophone sensor terminals 302 and MEMS sensor terminals
310) with different operating principles for receiving reflected
waves of the vibration applied to an inspection target in the
investigation area; and a processing apparatus 120 that detects the
internal structure of the inspection target by using the sensor
data received by the two kinds of sensors. The two kinds of sensors
are deployed in the investigation area with different densities, in
a distributed manner.
FIG. 3 illustrates an inter-geophone sensor distance 321 of the
plurality of geophone sensor terminals 302 included in the geophone
array and an inter-MEMS sensor distance 322. FIG. 3 shows that the
plurality of geophone sensor terminals 302 and the MEMS sensors are
deployed such that the following formula is hold. inter-MEMS sensor
distance 322>inter-geophone sensor distance 321 (1)
Incidentally, FIG. 3 illustrates distances in the left-right
direction in the drawing. The relation represented in formula (1)
also holds for the distances in the up-down direction in the
drawing.
The vibrator vehicle 100 outputs a sweep waveform in which the
frequency of the wave continuously varies, by using an oil pressure
mechanism. Signals from a reflection surface located underground is
captured by a large number of receivers (sensors) and thereby the
underground structure is estimated by computation. In this process,
results of an analysis using low frequency signals are important
information to estimate the underground structure. For this reason,
improvement of low frequency characteristics of vibrator vehicles
100 and receivers (sensors) is being vigorously promoted.
In the lower frequency region, the spatial resolution gets low due
to the wavelength of the frequency and therefore deployment of
receivers (sensors) with a high density is not so worthwhile in
estimating the stratum. Moreover, receivers (sensors) having low
noise density in the low frequency region, i.e., high-sensitivity
in the low frequency region, are recently available as MEMS sensor
terminals 310 or as low-frequency geophones. However, they are
expensive because of their introduction phase and low-frequency
geophones are assumed to be large in size and are limited in the
number of owned devices of each company.
Therefore, deploying sensors having high sensitivity in the lower
frequency region in a distributed manner is a preferable embodiment
in terms of reduction of exploration cost (reduction of exploration
period) and reduction of facility cost (MEMS sensor purchase
cost).
As the process of full wave inversion (FWI) gradually increases the
analysis frequency from a low frequency band to estimate the
underground structure, it is preferable that reliable information
be input to the computer for the low frequency band processed at
the beginning of the process. Therefore, by giving a validity
coefficient (reliability) to the data according to the sensitivity
of the sensor in each calculation frequency band on the basis of
the frequency characteristics of a sensor, it is expected that the
accuracy of estimating the underground structure increase.
FIG. 4 is a diagram illustrating another layout example in which a
plurality of types of sensor terminals are deployed. FIG. 4
presents more details than as presented in FIG. 3. As shown in
bottom left for example, when a rock or tree is present at a
location at which a sensor is planned to be deployed, the sensor is
arranged so as to avoid that location. The geophone array 301 is
connected with a cable 420 for transmitting data and supplying
power and the data is stored in data loggers 410, so that an
observer can check the status of the sensors as appropriate and
retrieve data (telemetry system).
On the other hand, each of the MEMS sensor terminals 310 shown in
FIG. 4 is of a nodal type and stores data in a storage unit thereof
after the MEMS sensor terminal 310 is deployed until the
exploration and the collection of the MEMS sensor terminal 310 are
finished. After the collection of the MEMS sensor terminals 310 is
finished, their data is collected via cables or via wireless
communication. It should be noted that, alternatively, the MEMS
sensor terminals 310 may be of a telemetry type and cables may be
used as their transmission lines.
When the number of MEMS sensor terminals 310 available in the
exploration is limited, it is preferable that, for the purpose of
the analysis, they be sparsely deployed as shown in the figure
rather than deployed together in one place.
In a reflection seismic exploration, on a path passing through a
shot point, a reflection point, and a vibration reception point, a
Fresnel zone is created in a surface area on a reflection surface.
In general, a structural variation smaller than this zone cannot be
identified, and thus the size of the Fresnel zone can be regarded
as a limit indicator of the horizontal resolution of the reflection
seismic exploration. This Fresnel zone depends on the relative
position between the shot point and the reception point, the
propagation velocity, and the wavelength of the wave. Thus, when
the elastic wave is a low-frequency wave, no matter how the
interval between reception points, i.e., interval between sensors,
is made narrow, it is not possible to obtain a spatial resolution
corresponding to the interval.
Therefore, when deploying sensors, it is preferable that the plan
for sensor deployment on the ground surface be created taking the
Fresnel zone into account. In the above-described arrangement
example, MEMS sensor terminals 310 having superior sensitivity
(low-noise density) in the low frequency band are deployed such
that the density of deployment is low. However, the deployment is
not limited thereto. An essential point of the present invention is
whether the density of deployment is low or high is determined on
the basis of the noise spectral density.
Next, a description will be given of an example of analysis method
for estimating underground structure on the basis of the data
obtained from sensors. A time required for an earthquake wave to
have traveled from the earthquake source to the observation point
is called "travel time" of the earthquake wave. A method of
estimating detailed earthquake wave velocity distribution in the
underground by utilizing travel time data is called travel-time
tomography. Here, ray tracing is used to calculate a theoretical
travel time from an assumed velocity model. Then, an inverse
problem is sequentially solved in such a way that the residual
difference between the theoretical travel time and an actually
observed travel time is minimized. As a result, the velocity model
is corrected. There are methods of obtaining physical property
distribution of an underground medium by inversion (estimation of a
model that fits with observation data, also called inverse problem)
using all the information including not only the travel time
information but also phases and amplitudes and the like of recorded
waves including succeeding waves. One of such methods is the
analysis method called full wave inversion (FWI) mentioned
above.
That analysis method is capable of estimating physical property
distribution (velocity, density, attenuation and the like) more
accurately with higher resolution than travel-time tomography
because the method uses more information for the analysis. This
method was proposed in the 1980s and has been actively researched
in the latter half of 1990s. In recent years, the method has been
put into practical use with efficient algorithms and improved
computational capabilities.
Although this analysis method is capable of estimating
high-resolution physical properties, the method tends to fall into
a local optimal solution due to its non-linearity and high
dependency to the initial model. An effective measure to this
problem is to use data including low frequency component (2 Hz to 3
Hz) with a long offset distance (distance between the earthquake
source and the vibration reception point). Furthermore, by using a
highly accurate initial model using a highly accurate and highly
reliable low frequency component, it is possible to prevent falling
into a local optimum solution. Note that, it is common to perform
analysis while shifting the frequency band from low frequency
components to high frequency components, in order to perform a
stable analysis. In addition, the analysis is performed further by
adding information about succeeding waves. In summary, in the case
of analysis using the full wave inversion, the reliability of the
data in the low frequency region greatly contributes to the
accuracy and reliability of the final underground analysis.
A high sensitive sensor is capable of catching a reflected wave
reflected from a reflection surface at a deeper depth under the
ground and thus is effective for analyzing an analysis target
located at a deeper depth. In contrast, solving an inverse problem
using a sensor with low sensitivity may result in falling into a
local optimal solution. Therefore, in a case of analysis of a
target located at a deeper depth, by preferentially using
high-sensitivity sensors for the analysis in accordance with
validity coefficients (reliability) representing the sensitivity of
the sensors, more reliable underground image can be obtained. The
scope of the target to be analyzed by data which has been limited
or preferentially selected is specified by parameters. The values
of the parameters may be varied on the basis of data obtained from
other means, e.g., data produced in the past, core sample data of
the past, or analysis data of the past or on the basis of the
knowledge of a geologist and/or an authority having experience of
exploration, in order to derive solution which is believed to be
correct rather than a local optimal solution. In other words, a
preferred output can be expected by preferentially applying sensor
data for the calculation in a manner that depends on frequency
sensitivity characteristics of sensor (e.g., noise spectral
density) and the depth of the analysis target.
FIG. 5 is a diagram illustrating assumed sensor characteristics
(noise spectral density) of the geophone array 301 and a MEMS
sensor terminal 310. FIG. 6 is a diagram illustrating an example of
a sensor characteristics (noise spectral density) of the geophone
array 301, showing a case in which the sensor characteristics has
been improved from that shown in FIG. 5. FIG. 7 is a diagram
illustrating an example of a sensor characteristics (noise spectral
density) of the MEMS sensor terminal 310, showing a case in which
the sensor characteristics has altered from that shown in FIG.
5.
FIG. 5 is a diagram illustrating the sensor characteristics
information (noise spectral density) of the MEMS sensor terminal
310 and the geophone array 301 constituted by the plurality of
geophone sensor terminals 302. The sensor characteristic curve 510
of the MEMS sensor terminal 310 shows that the noise density
thereof is low in a region from a low frequency region to a certain
frequency in comparison with the sensor characteristic curve 501 of
the geophone array 301.
The MEMS sensor terminal 310 is a device with a micron-level
structure in which a sensor with mechanical elements, an actuator,
and an electric circuit are gathered and is normally provided as a
set in which an ASIC circuit is included. The MEMS sensor terminal
310 carries out measurement while applying a feedback to a sensor
output. The ASIC circuit causes a noise in which 1/f noise of
amplifier is dominant in a low frequency region and quantization
noise of A/D converter is dominant in a high frequency region. As a
result, the ASIC circuit has noise spectral density like the sensor
characteristic curve 510 illustrated in FIG. 5.
FIG. 6 illustrates the sensor characteristic curve 501 of the
geophone array 301, a sensor characteristic curve 501a of a
geophone array constituted by low-frequency geophone sensors
designed and implemented so as to obtain good sensitivity in a low
frequency region, a sensor characteristic curve 501b of a geophone
array constituted by low-frequency geophone sensors designed and
implemented so as to obtain good sensitivity in a high frequency
region, and the sensor characteristic curve 510 of the MEMS sensor
terminal 310.
FIG. 7 illustrates the sensor characteristic curve 501 of the
geophone array 301, and the sensor characteristic curve 510 of the
MEMS sensor terminal 310 as well as a sensor characteristic curve
510a of a MEMS sensor whose sensitivity has degraded due to the use
in fields and/or aged deterioration. The sensor characteristic
curve 510a is drawn by plotting the values of the noise spectral
density at measurement frequencies measured in a regular inspection
of the sensor.
As shown in FIG. 5, in general, the noise density of the MEMS
sensor terminal 310 is lower than that of the geophone array 301,
particularly in a low frequency band. In addition, the noise
density is lower than that of the geophone array 301 even in a high
frequency band.
In contrast, as shown in FIG. 6, the geophone sensor terminal 302
(see FIG. 3) included in the geophone array 301 can be configured
to have a specific frequency band in which its maximum sensitivity
is obtained by setting the size of the coil and/or the magnet.
However, it should be noted that as the frequency band is specified
lower, the size gets bigger, resulting in the difficulty in
implementation.
On the other hand, as shown in FIG. 7, the sensor characteristics
information of the MEMS sensor terminal 310 shows alteration, due
to degradation of the MEMS sensor terminal 310, from that of a new
MEMS sensor. In this case, it is foreseen that the noise density
will increase (the sensitivity will be degraded). For this reason,
it is a matter of concern how to utilize a large number of sensor
terminals with different noise spectral densities.
FIG. 8 is a diagram illustrating the frequency characteristics of
the sensor terminals and an example of utilization of data used in
calculation. FIG. 9 is a diagram illustrating the frequency
characteristics of the sensor terminals and an example of another
utilization of data used in calculation. FIGS. 8 and 9 are diagrams
presenting how the information obtained from the geophone array 301
and/or the MEMS sensor terminals 310 can be utilized using the
information shown in FIG. 5.
FIG. 8 represents an embodiment that utilizes the sensors of both
the geophone array 301 and the MEMS sensor terminals 310 in such a
manner that only MEMS sensor terminals 310 having good sensitivity
in a low frequency region are used in the low frequency region; and
that the higher the frequency of the frequency component utilized
in the calculation, the number of members in the geophone array 301
whose data is utilized for calculation is increased.
FIG. 9 represents an embodiment that utilizes the sensors of both
the geophone array 301 and the MEMS sensor terminals 310 in such a
manner that the number of members in the geophone array 301 whose
data is utilized for calculation is gradually increased as the
frequency used for the calculation is increased, rather than
utilizing the data of geophone array 301 suddenly for frequencies
higher than a certain frequency region.
According to the present embodiment, as shown in FIGS. 3 and 4, as
the MEMS sensor terminals 310 are sparsely deployed in a
distributed manner in the investigation area, the absolute number
of the MEMS sensor terminals 310 is small. For this reason, the
embodiment effectively uses the data of the geophone array 301 in
the high frequency region. As discussed above, as the process of
full wave inversion (FWI) gradually increases the analysis
frequency from a low frequency band to estimate the underground
structure, it is preferable that reliable information be input to
the computer for the low frequency band processed at the beginning
of the process. Therefore, by assigning validity coefficients
(reliability) to data in accordance with the sensitivities at the
calculation frequency bands, on the basis of the frequency
characteristics of sensors, it is expected that the accuracy of
estimating an underground structure be improved.
FIG. 10 is a diagram illustrating an example of resource
exploration, in a three dimensional fashion. Reference numeral 701
designates a reference plane located at the depth to be analyzed.
An elastic wave generated from the vibrator vehicle 100 is
attenuated as it is propagated to a deep stratum. Therefore, the
depth that can be sensed by a sensor depends on the sensitivity of
the sensor. Therefore, it is preferable to utilize high sensitive
sensors and their data in exploration and analysis of deeper depth.
However, when the target layer is a complex stratum, such as one
that is multi-layered and not homogeneous or includes a fault, it
acts as a factor that prevent signal propagation, and therefore the
depth is not a sole parameter. Therefore, it is preferable to
utilize data in accordance with the depth and the complexity of the
stratum.
On the other hand, although there observed many cases in which the
substantial complexity of the stratum can be estimated on the basis
of data of the past or by another means, the depth is still a major
parameter to be taken into account because the purpose of
exploration is to clarify the stratum structure. In addition, these
depend on the target area as well as the type of petroleum
(conventional type; non-conventional type). In view of the
foregoing, it is preferable to select data to be utilized in
accordance with validity coefficients (reliability) on the basis of
the sensitivity information of sensors and the depth and complexity
and the like of the target to be analyzed. The above-described
process may be executed by a computer program automatically or by
an analysis person tuning the parameters on the basis of visualized
data and other means.
FIG. 11 is a diagram illustrating which sensors are to be used in
resource exploration. The horizontal axis represents the depth; and
the vertical axis represents the frequency. A description will be
given a case in which geophone sensors and MEMS sensors are used as
sensors. For a shallow depth, high sensitivity (low noise density)
is not required. For a deep depth, however, high sensitivity is
required. The MEMS sensors have higher sensitivity in the low
frequency region than the geophone sensors. For this reason, using
data from the MEMS sensor for a case of a deeper depth and a lower
frequency region is more likely to perform the resource exploration
with high accuracy.
An exploration system, which is an internal structure detection
system of the present embodiment, includes: an artificial seismic
source; a plurality of sensor terminals (e.g., sensor terminals
103) having different frequency characteristics; a computer (e.g.,
processing apparatus 120) performing an algorithm for analyzing the
obtained data. Each of the plurality of sensor terminals includes a
speed sensor or an acceleration sensor (e.g., geophone sensor
terminal 302 and MEMS sensor terminal 310) which detects vibration
generated by the artificial seismic source and which is assigned
with a unique sensor ID. Each of the plurality of sensor terminals
includes a storage device (e.g., storage unit 122) in which the
frequency sensitivity information (sensor characteristics
information) of the sensor terminal is stored; alternatively, the
frequency sensitivity information of each of the plurality of
sensor terminals is stored in a common storage device or is
registered with a database, in association with the sensor
identifier of the sensor terminal.
The sensors capture the elastic wave emitted from the artificial
seismic source as a velocity or an acceleration, and record
captured data in their storage devices. The computer renders an
underground image with high accuracy by performing analysis using
an algorithm that determines the underground structure in an
explorative manner on the basis of the captured data and the sensor
characteristics information.
The data to be inputted to the analysis is selected on the basis of
indicators representing the sensor characteristics information and
the analysis target, e.g., a depth. A plurality of sensors
constituting a sensor group is deployed at positions determined on
the basis of its noise spectral density to obtain highly accurate
underground images.
By providing an optimal layout for the sensors used in the resource
exploration according to the present embodiment in accordance with
the noise spectral density of the sensors and assigning validity
coefficients to data obtained by the sensors, an improvement is
expected in the estimation of the underground structure. That is,
by optimal deployment of the sensors used in the resource
exploration and assignment of validity coefficients to the sensor
characteristics information when performing an analysis using the
deployed sensors, it is possible to obtain highly accurate
underground images by the given sensor terminals and the data
associated therewith.
The present embodiment has been described taking a resource
exploration system as an internal structure detection system by way
of example. However, the embodiment is not limited thereto. For
example, the embodiment may be applied to a water leakage
monitoring system or to a structural health monitoring system. The
water leakage monitoring system is a system for detecting water
leakage points of a water pipe or the like laid under a road. The
water leakage monitoring system includes: two kinds of sensors with
different operating principles for receiving reflected waves of
vibration applied to an inspection target (water leakage point) in
an investigation area; and a processing apparatus that detects the
internal structure of the inspection target by using the sensor
data received by the two kinds of sensors. The two kinds of sensors
are deployed in the investigation area with different densities, in
a distributed manner.
The structural health monitoring system is a system for diagnosing
the structural performance of a construction on the basis of a
response characteristics thereof against an earthquake and/or
microtremor, using acceleration sensors or the like deployed on the
construction. The structural health monitoring system includes: two
kinds of sensors with different operating principles for receiving
reflected waves of vibration applied to an inspection target in a
construction; and processing apparatus that detects the internal
structure of the inspection target by using the sensor data
received by the two kinds of sensors. The two kinds of sensors are
deployed in the investigation area with different densities, in a
distributed manner.
REFERENCE SIGNS LIST
100 Vibrator Vehicle 101 group of vibrator vehicles 102 shot point
103 sensor terminal 104 movement path 105 satellite 106 observation
vehicle 110 reservoir 120 processing apparatus 121 processing unit
122 storage unit 122A sensor inspection information 122B,122C,122D
sensor characteristics information 201 MEMS acceleration sensor 202
acceleration signal processor 203 external I/F 204 processor 205
storage unit 206 GPS receiver 207 environmental sensor 208 battery
301 geophone array 302 geophone sensor terminal 303
analog-to-digital convertor (ADC) 310 MEMS sensor terminal 321
deployment interval of geophone array 322 deployment interval of
MEMS sensor terminals 410 data logger (data collection device) 420
cable for data transmission and power supply 501,501a,501b,510,510a
sensor characteristic curve 701 reference plane at depth of
analysis target
* * * * *